Search bioRxiv⌕ Search

Biology subjects

Moore, F. E.

Publications and source records attributed to Moore, F. E..

2 recordsLinked to original sources

A laboratory module that explores RNA interference and codon optimization through fluorescence microscopy using Caenorhabditis elegans

Scientific research experiences are beneficial to students allowing them to gain laboratory and problem-solving skills, as well as foundational research skills in a team-based setting. We designed a laboratory module to provide a guided research experience to stimulate curiosity, introduce students to experimental techniques, and provide students with foundational skills needed for higher levels of guided inquiry. In this laboratory module, students learn about RNA interference (RNAi) and codon optimization using the research organism Caenorhabditis elegans (C. elegans). Students are given the opportunity to perform a commonly used method of gene downregulation in C. elegans where they visualize gene depletion using fluorescence microscopy and quantify the efficacy of depletion using quantitative image analysis. The module presented here educates students on how to report their results and findings by generating publication quality figures and figure legends. The activities outlined exemplify ways by which students can improve their critical thinking, data interpretation, and technical skills, all of which are beneficial for future laboratory classes, independent inquiry-based research projects, and careers in the life sciences and beyond.

scientific communication and education↗

Mortality prediction in sepsis via gene expression analysis: a community approach

Improved risk stratification and prognosis in sepsis is a critical unmet need. Clinical severity scores and available assays such as blood lactate reflect global illness severity with suboptimal performance, and do not specifically reveal the underlying dysregulation of sepsis. Here three scientific groups were invited to independently generate prognostic models for 30-day mortality using 12 discovery cohorts (N=650) containing transcriptomic data collected from primarily community-onset sepsis patients. Predictive performance was validated in 5 cohorts of community-onset sepsis patients (N=189) in which the models showed summary AUROCs ranging from 0.765-0.89. Similar performance was observed in 4 cohorts of hospital-acquired sepsis (N=282). Combining the new gene-expression-based prognostic models with prior clinical severity scores led to significant improvement in prediction of 30-day mortality (p<0.01). These models provide an opportunity to develop molecular bedside tests that may improve risk stratification and mortality prediction in patients with sepsis, improving both resource allocation and prognostic enrichment in clinical trials.

bioinformatics↗